Intellectual Property Law: Costs vs Benefits

Nothing I say here is original; it is heavily influenced by Tabarrok's thinking on the matter. This post started as a Facebook comment in response to a friend's question; I am putting it here so I will be able to find it again and refer to it easily.

The question was "To what extent should governments try to enforce intellectual property rights? ... How would we determine, in principle, whether intellectual property laws are a good idea for governments to keep enforcing? (And, what's your best guess as to what we should be doing right now?)"

The default Economist answer to any question of the form 'To what extent should governments do X?' is always 'Until the marginal costs of doing more X start to exceed the marginal benefits.' I am only half joking when I say that the procedure for getting an Economics PhD is for people to drill the decision procedure 'Do things until marginal costs exceed marginal benefits, then stop.' into your head until it would be the first thing you mumble if you got dragged out of bed in the middle of the night and asked a question of this form.

The marginal cost of each additional year of intellectual property protection is the monopoly deadweight loss, plus the loss of knowledge diffusion and the innovations that would have been created based on the thing if it was a public good. This latter term is often dramatically underestimated. This marginal cost is probably roughly constant over time for most things, but will increase over time for important foundational innovations.

The marginal benefit is the incentive to innovate and create the thing that is generated by the difference between the monopoly profits under the government-IP system and the state of nature where people keep things hidden. Note that monopoly profits are not the same as the deadweight loss; they are just a transfer and therefore not a social cost. This marginal benefit decreases over time; older IP is almost always less valuable to a monopolist because substitutes will be developed.

Finding the exact point where marginal benefit equals marginal cost is always tricky in practice, but this gives us a few obvious guidelines:

Different types of IP should have different types of IP laws. IP law should be based on how expensive something is to create, how likely people are to create it anyway for non-profit motives, what the expected profit flow looks like, and how valuable it would be in the public domain.

Some things should not get any IP protection. Others should get a lot.

If 90% of the profits from a thing come in the opening weekend, and it takes more than a week to copy it, there is no need for IP. (If copying is instant, the proper length for IP protection is a few weeks.)

If we routinely see individuals producing a thing without any expectation of payment, and/or producing it is cheap and brings status rewards, there should be no IP protection. All it does is further reward people who won the attention/status lottery.

The 20-year patent for expensively researched industrial processes seems like a decent balance when the market for the product is small; larger markets and easier scale-up imply shorter optimal patent terms.

Further exploration can be left as an exercise for the reader.

The Story of a Lucky Economist

The career guide 80,000 Hours highly recommends getting an Economics PhD. I completely agree with their assessment. If you have an academic personality and a decent work ethic, and are lucky enough to be born with high analytical intelligence, there are few better options. If you identity as an Effective Altruist and want to make the world a better place, becoming an economist (and doing anything other than teach at a low-ranked school) is an excellent career choice for earning to give (collecting a good paycheck and giving a lot to charity) and, if you get lucky, can also be good for direct impact (personally making the world a better place.)

I got lucky. This is the story of my direct impact as an economist working for a US federal government agency. Please do not expect that this is likely to happen to you if you become an economist and start to work for a government. But something like it might happen.

Before I tell my story, I will tell the story of C, one of the veteran economists in our office. When she was interviewing me for the job and telling me about the work that they do, she told the story of her direct impact, which I will heavily paraphrase.

When the agency produces a regulation, they get scientists, lawyers, and policy experts together in a room to write the rule. If the team is well-managed and/or the economists are good at making friends, there will also be an economist in the room.

C was in the room when they were writing rules for irrigation water quality. There was some concern that pathogens in the irrigation water would contaminate the final food, so the room's consensus was that they would require all irrigation water to meet the same standards as drinking water. Then C started asking questions.

C: "Are we going to apply this rule to drip irrigation?"
Someone: "Yes, I don't see why not."
C: "Are we going to ban people from fertilizing plants with manure?"
Someone: "No, of course not, it is safe under the right conditions."
C: "So, you are writing a rule that would force people to put drinking water on manure?"

Everyone else looked at each other, and then realized that they should loosen the water quality standards under certain conditions.

C explained that one of our main jobs as agency economists was to think about the big picture and be the voice of 'common sense' in the room. It is surprisingly difficult to find people who can do this, and the 'PhD Economist' credential is a signal that you might be the kind of person who can escape groupthink and see neglected but important side effects and chains of causality.

This story was perhaps the highlight of her 20-year career in the agency. She probably saved farmers tens of millions of dollars, in total. In that one conversation, she repaid the country the value of her entire career's salary. This is what you can expect your direct impact to be as an agency economist: find ways to save people money and make life a little less difficult for them, while still accomplishing the mission of the agency. This is a good life and a noble calling, for those who think about efficiency and the big picture, and it is unreasonable to expect more.

I got more.

One day, the boss asked me to do a quick estimate of the costs and benefits of removing the GRAS (generally recognized as safe) status of PHOs (partially hydrogenated oils). You may know this as the 'trans fat ban'. Because this was technically just an exercise of existing authority and not a new regulation, it did not need an official economic analysis, but management thought it would be good to have some idea of the numbers before moving forward.

I did not know what to expect at first, but after I did the research, I found that the numbers would be huge. The costs would be measured in billions, and the lives saved would be measured in tens of thousands. There was very strong scientific evidence that trans fats are uniquely toxic among all commonly used food additives, and banning them would be the biggest public health action on decades. As a conservative estimate, they were killing eight Americans every day.

Once I realized this, I was consumed with a need to do everything I could to make the action publish as soon as possible, while making sure that it and my analysis would survive any legal challenge. I was shocked that few others in the agency realized how big and how important this was. Most of management just saw it as another thing on a big list of things that the agency was doing, and either did not know or care about the numbers involved. There was nothing I could do to push it out faster, aside from explaining the rule's huge positive effects to everyone who would find me credible, which I did. But talk like that is common in the agency, because everybody wants to push out their favorite regulation.

But I could make a real positive impact by making absolutely sure that I was not delaying the action. I studied the process, identified the likely times when things would stop because people were waiting for economic numbers, and prepared for those times. I made sure that all of my spreadsheets were flexible and complete enough to, with a few minutes' work, accept any variation in inputs and produce new output tables. I occasionally wrote several versions of the analysis ahead of time while waiting for management decisions, one for each plausible decision, so that I could turn it around in hours of being notified of the decision. When necessary, I worked lots of overtime to get things out the next day.

Basically, I identified the times when I was on the critical path, and did everything possible to shorten the time on that critical path. I do not know how successful I was. But even if I got the rule out one week faster, I saved over 50 lives. And if my analysis and contributions made the rule 0.1% more likely to survive a court challenge, then I saved over 25 lives. Direct impact numbers can get scary large when dealing with major public health initiatives that cause a single-digit percentage change in the heart attack rates of a country of 300 million.

However, depending on how you choose to interpret the Value of a Statistical Life, my second direct impact may have actually saved more lives.

Congress had passed a law requiring the agency to pass several major new regulations. The agency scheduled the regulations, starting with the most important ones that they had the most knowledge of. For the rest, they gathered information and started talking to a lot of affected producers to figure out what to do. One part of this law was a mandate to write rules about a thing that no agency in the world had ever dealt with before, and this was scheduled to be last, after a lot of research and discussion.

However, a consumer group sued the agency to publish the regulations faster. The agency lost, and was handed a court-ordered deadline for all rules, including the novel rule.

I was pulled off all other projects and assigned to this rule full-time. There were about a dozen of us in the room writing the first draft over a period of about two weeks. People would propose ideas and ask me questions about likely effects. I would poke around on the Internet and/or our internal databases for about an hour, crunch some numbers, and then give them a rough estimate.

It quickly became apparent that, even though everyone was trying to write the rule to cause as little burden as possible, following our congressional mandate would cost a lot of money.

Most rules exempt very small businesses from most or all requirements. In past rules, 'very small business' was typically defined as having up to $250,000 to $1 million in annual sales. I suggested raising the 'very small business' threshold and gave a menu of options, with cost savings and market coverage for a variety of cutoffs from $1 million up to $50 million. I went that high not because I expected anyone to choose that option, but because I understand framing and anchoring.

The team chose a threshold of $10 million in annual sales, which would save about $150 million a year compared to a $1 million cutoff, and still cover over 97% of the market by sales volume. After working to find plausible legal and scientific reasons that agency lawyers could use to justify this precedent-breaking number in court if necessary, the team agreed to propose the change to management, and management agreed.

My understanding of the power-law distribution of firm sizes, and my ability to communicate its practical effects, had saved small businesses about $150 million a year in compliance costs.

The Value of a Statistical Life in the USA is about $10 million. People will, on average, spend about $10,000 to reduce their chances of dying by one in a thousand. By saving people $150 million a year in compliance costs, I gave them enough resources to invest in things that are expected to save 15 lives a year. Assuming that the rule lasts for about 30 years before being rewritten, I saved the statistical equivalent of 450 lives with a couple insights and a few days of work.

Of course, I also caused a small increase in the chance that a very unlikely but horrible thing would happen. The increased chance, multiplied by the base rate and the expected casualties and other economic costs, means that I caused the statistical equivalent of about 50 deaths by encouraging the team to exempt small producers. So I can 'only' claim about 400 lives saved on net.

I do not expect anything like this to happen again in my career. I was lucky enough to be in the right place at the right time, twice. Laws and regulations with that much impact only come along about once a decade on average. For the rest of my life, I will be doing much smaller things.

The main message of this story is that the direct impact of a government economist is extremely high-variance. Most of the time, you will do nothing to make the world better. Occasionally, you will do something that prevents a few million dollars from being wasted. And if you get very very lucky, you might do something that saves the statistical equivalent of hundreds of lives.

Technical Appendix for Effective Altruists

If you are a PhD economist working for the US federal government, you will typically start at GS-12 and quickly work your way up to GS-14 (currently about $120,000 a year in the DC area). Then you will stay at GS-14 for the rest of your career unless you work your way up to senior management. This is less than private industry and consulting, although not that much less when you consider the value of benefits, and is much less stressful and time-consuming. I have an excellent lifestyle, and earn enough to painlessly give over $30,000 a year to charity.

If you are the kind of person who wants to work 80 hours a week and make a name for yourself, earn lots of money, and/or have more direct impact, then I still recommend starting your career as an agency economist. Government work does not force you to work as hard as industry or academia just to stay afloat. You will have extra time and energy in your week, so you can, if you choose, use that time for self-directed career advancement and make your agency job an excellent springboard to many different high-flying careers. I personally have no plans of exercising this option, but I know many people who have. Some publish lots of papers, others network and get promoted in the government, and others leave for high-paying mid-career private-sector jobs.

Everything I have discussed is only relevant to agencies where economists are actually involved in making policy decisions or new regulations. There are some places in government that are just research shops, pushing out academic publications. Avoid them. Other places have economists churn out standard reports and analyses for people to use. I do not know how impactful this is, but it is probably still a good job.

I have been able to use my knowledge to help with Effective Altruism Policy Analytics, and to give advice to many people in the LW/EA community. If you have further questions, feel free to ask in the comments here or in another location. If you are seriously considering this career, I am available to talk. I am also available as a dissertation advisor for any EA-affiliated PhD students aiming for an agency career (having advisors outside your school is an excellent signal, and I have worked on government hiring committees and know what they look for in job market papers).

Information Processing

The thing that truly terrifies me about the election is that it reveals that our best methods of understanding and predicting reality are fundamentally inadequate. Brexit was the first hint, and this is the confirmation.

Trump's victory was not due to random chance, or weather, or some last-minute surprise. Nothing really changed in the last week. The depth of his popular support was a fact of reality days before the election, and a competent information-processing system would have learned of it.

It is a known fact among social scientists that polling always underestimates support for things that are seen as violating social norms. I assumed that the polls or models corrected for this somehow. They did not.

The polling was wrong. All of it. The experts were wrong. All of them. (Trump supporters predicting victory don't count, because partisans always believe they will win. And a few people online don't count, either, because you can find any claim online.) But what truly scares me is that the markets were wrong. All of them.

Before the election, I knew of the polling bias issue and worried that Trump had more popular support than the polls showed. But I did not place any bets, because I assumed that markets had already priced in this information. They had not. In the early afternoon of election day, election prediction markets had Trump at about 25%, and the stock and foreign exchange markets were assuming a Clinton victory with high probability.

Hedge funds spent millions of dollars on private polling and models to gain information that would allow them to beat the stock and forex markets. This is exactly how markets are supposed to reveal information. They give people billion-dollar incentives to obtain information, and then when people trade on that information, the price moves to reflect the private knowledge.

But even with the best performance incentives known to humanity, and all of the tools of social science and modern technology at their disposal, the hedge funds failed to obtain a basic fact about reality. If we cannot obtain the truth about people's feelings in a situation where the stakes are this high and information is so readily available, how can we hope to predict how our actions and policies will affect people's subjective quality of life?

AI Safety Optimal Investment

It occurred to me today that I have never seen any attempt at calculating how much society should be willing to spend to prevent an AI Catastrophe. I am pretty good at this kind of thing, so here's a quick Fermi estimate:

Conceptually, this is a lot like buying a life insurance policy for the human race. There is some probability of a catastrophe, so the annual amount we should be willing to pay for insurance is the cost of the catastrophe times its annual probability.

First, we need the dollar value of the human race. This is relatively easy. We have revealed-preference estimates for the value of a flourishing human life in a rich society, so that should be about right for estimating the value people place on a good personal and genetic future, assuming a future where everyone's quality of life is about as good as the average citizen of the US in the early 21st century.

The Value of a Statistical Life in the US is about $10 million, or $1x10^7. Over the relevant time frame in which an AI catastrophe is likely, world population will likely be stabilizing at about 10 billion, or 1x10^10. So the value of the human race, in current US dollars, is about $1x10^17.

Now we find the probability of a catastrophe. For convenience, I use numbers presented in the article linked above, which is a pretty good summary of the current understanding of the field.

Artificial Superintelligence is unlikely to happen before 2025, but almost certain to arrive by 2125. So each year in that timeframe, there is a 1 in 100, or 1x10^-2, chance of ASI.

Now the numbers get very speculative, so in the grand tradition of Fermi estimates I will simply round everything to the nearest power of ten and say that there is a 10% chance that, when ASI develops, it will, without any safeguards, destroy the human race.

So, given a 1x10^-3 annual chance of losing something valued at $1x10^17, we should be willing to spend $1x10^14 to prevent that possibility.

$100 trillion a year is a lot of money. That is almost exactly equal to the total value of the entire world economy. It tells us that any policy short of destroying the entire world economy and/or killing millions of people would be worth doing if it prevented an ASI catastrophe with 100% certainty [Edit: assuming that the policy does not make other existential risks significantly more likely]. For example, a total ban on producing or maintaining any kind of computer, combined with a credible threat to nuke anyone who violates this ban, is a legitimate policy option with a positive expected payoff.

More realistically, we will continue on our current technological course and try to make things safer. Even if AI research only had a 1 in 100 chance of guaranteeing Friendly AI, we should be willing to pay a trillion dollars a year for it.

IBM Watson Personality Insights

IBM claims that it can provide insights to my personality by analyzing the text I write. Let's test this by separately feeding it the text of my last few blog posts and seeing what it says about each one:

Organ Donor Safety Exemptions:

You are shrewd, unconventional and can be perceived as indirect.

You are imaginative: you have a wild imagination. You are laid-back: you appreciate a relaxed pace in life. And you are intermittent: you have a hard time sticking with difficult tasks for a long period of time.

Your choices are driven by a desire for prestige.

You are relatively unconcerned with tradition: you care more about making your own path than following what others have done. You consider helping others to guide a large part of what you do: you think it is important to take care of the people around you.

Well-Being Analysis:

You are shrewd, inner-directed and can be perceived as indirect.

You are unconcerned with art: you are less concerned with artistic or creative activities than most people who participated in our surveys. You are intermittent: you have a hard time sticking with difficult tasks for a long period of time. And you are imaginative: you have a wild imagination.

Your choices are driven by a desire for prestige.

You are relatively unconcerned with both taking pleasure in life and tradition. You prefer activities with a purpose greater than just personal enjoyment. And you care more about making your own path than following what others have done.

So far, pretty consistent. But both of those blog posts were technical academic analysis. What happens when I give it a first-person account of a more emotional experience?

Chen Guangcheng:

You are social, boisterous and unconventional.

You are empathetic: you feel what others feel and are compassionate towards them. You are assertive: you tend to speak up and take charge of situations, and you are comfortable leading groups. And you are confident: you are hard to embarrass and are self-confident most of the time.

Your choices are driven by a desire for efficiency.

You are relatively unconcerned with tradition: you care more about making your own path than following what others have done. You consider helping others to guide a large part of what you do: you think it is important to take care of the people around you.

That is a big change in the 'you are' and 'driven by' lines, and the first paragraph is entirely different. The only commonality is the 'unconcerned with tradition' and 'helping others' parts of the last paragraph.

Now, what does it think about my account of going out and helping people who got their cars stuck in the snow?

Car Shoving:

You are heartfelt.

You are empathetic: you feel what others feel and are compassionate towards them. You are unconcerned with art: you are less concerned with artistic or creative activities than most people who participated in our surveys. And you are calm-seeking: you prefer activities that are quiet, calm, and safe.

Your choices are driven by a desire for well-being.

You consider helping others to guide a large part of what you do: you think it is important to take care of the people around you. You are relatively unconcerned with tradition: you care more about making your own path than following what others have done.

Again, it says something almost completely different. It is interesting to note that the personality analysis for the last two are backwards. I think that this paragraph describes the Me who attended the Chen Guangcheng talk, and the previous one describes the Me who went out to shove cars. It is particularly funny that it reacts to my description of shoving cars around in hilly slippery roads by saying 'you prefer activities that are quiet, calm, and safe'.

Now we go back to a more a more analytical post, but a different kind of analysis:

Media Musings:

You are shrewd, somewhat inconsiderate and can be perceived as indirect.

You are laid-back: you appreciate a relaxed pace in life. You are carefree: you do what you want, disregarding rules and obligations. And you are imaginative: you have a wild imagination.

Your choices are driven by a desire for efficiency.

You consider both independence and taking pleasure in life to guide a large part of what you do. You like to set your own goals to decide how to best achieve them. And you are highly motivated to enjoy life to its fullest.

The first line looks like its reaction to my other bits of analysis, and the 'desire for efficiency' is a repeat, but the rest is mostly things it has not said about me before. What might it say next?

Confusing Social Norms

You are a bit inconsiderate, somewhat critical and excitable.

You are melancholy: you think quite often about the things you are unhappy about. You are intermittent: you have a hard time sticking with difficult tasks for a long period of time. And you are unconcerned with art: you are less concerned with artistic or creative activities than most people who participated in our surveys.

Your choices are driven by a desire for connectedness.

You consider helping others to guide a large part of what you do: you think it is important to take care of the people around you. You are relatively unconcerned with taking pleasure in life: you prefer activities with a purpose greater than just personal enjoyment.

Some new and different stuff, some repeats. That post was more of an expression of confusion and questioning than an account or analysis. Let's chalk that one down to the small sample size, it was just 360 words after I chopped out the quotes and links.

The next one should be more informative, as it combines analysis and first-person accounts, and talks about something that is more connected to my identity:

Lego in Asia

You are inner-directed and skeptical.

You are calm-seeking: you prefer activities that are quiet, calm, and safe. You are empathetic: you feel what others feel and are compassionate towards them. And you are deliberate: you carefully think through decisions before making them.

Your choices are driven by a desire for prestige.

You are relatively unconcerned with both taking pleasure in life and tradition. You prefer activities with a purpose greater than just personal enjoyment. And you care more about making your own path than following what others have done.

The overall analysis sounds familiar and kind of accurate, but it jumps out at me that it says 'you are skeptical' to a blog post that I would characterize as being filled with the wonder of shared experience and progress and connectedness. It has not said that about any other post.

Cargo Cult Crafts:

You are excitable.

You are laid-back: you appreciate a relaxed pace in life. You are empathetic: you feel what others feel and are compassionate towards them. And you are calm-seeking: you prefer activities that are quiet, calm, and safe.

Your choices are driven by a desire for well-being.

You are relatively unconcerned with tradition: you care more about making your own path than following what others have done. You consider independence to guide a large part of what you do: you like to set your own goals to decide how to best achieve them.

Okay, that one just confused the algorithm. It says that I am both excitable and laid-back, that I 'prefer activities that are quiet, calm, and safe' when the entire focus of the blog post is about how I like to hack at real pumpkins with real knives and learn by taking risks, and that I am 'unconcerned with tradition' when I defend the traditions of my childhood against a shallow commercial substitute.

Its reaction to Important Information, Important Caveat is about the same as its reaction to most analysis posts, so no sense repeating it. But I want to feed it one last post, a rant, to see how it reacts:

Whack Rant

You are a bit compulsive, somewhat critical and skeptical.

You are intermittent: you have a hard time sticking with difficult tasks for a long period of time. You are unconcerned with art: you are less concerned with artistic or creative activities than most people who participated in our surveys. And you are melancholy: you think quite often about the things you are unhappy about.

Your choices are driven by a desire for efficiency.

You consider achieving success to guide a large part of what you do: you seek out opportunities to improve yourself and demonstrate that you are a capable person. You are relatively unconcerned with tradition: you care more about making your own path than following what others have done.

That is actually about how you would expect someone to react to an intellectual takedown of something I hated.

Overall, it is pretty clear that the system does not have any deep personality insights. It is reacting almost entirely to the rhetorical choices I make for each particular post. Anyone who has any writing skill or understanding of rhetoric knows that you should use a different voice, tone, and approach in different situations.

The only thing consistently output, in eight of ten writing samples, was that I am relatively unconcerned with tradition. Nothing else showed up in more than half of the results. This probably reflects my consistent use of scientific and analytical language.

Five of the results claimed that 'You are intermittent: you have a hard time sticking with difficult tasks for a long period of time.' and another five claimed 'You are unconcerned with art: you are less concerned with artistic or creative activities than most people who participated in our surveys.' I consider both of these claims to be dubious, and I am not really sure where they came from.

Four of the results claimed that I am shrewd, empathetic, driven by prestige, guided by helping others, and/or unconcerned with taking pleasure in life. Again, that seems kind of random and not really connected to who I am.

As a final bit of fun, let me plug the output of the system into its input, so we can see what it says about itself:

You are confident and generous.

You are assertive: you tend to speak up and take charge of situations, and you are comfortable leading groups. You are calm under pressure: you handle unexpected events calmly and effectively. And you are respectful of authority: you prefer following with tradition in order to maintain a sense of stability.

Experiences that give a sense of well-being hold some appeal to you.

You are relatively unconcerned with tradition: you care more about making your own path than following what others have done. You consider helping others to guide a large part of what you do: you think it is important to take care of the people around you.

Organ Donor Safety Exemptions

The following question was posted on the EA Policy Facebook page:

General idea: should exemptions be granted to certain safety laws if you sign up to be an organ donor?

Would such policies likely be a positive or negative for organ donation and safety compliance?

My response turned into a full blog post:

The biggest possible failure is that it discredits the idea of voluntary organ donation, resulting in less organ donation from other sources. I have no idea how big this effect might be.

Concrete examples help analysis, so I will consider this by looking at motorcycle helmet laws:

It is possible that telling people 'We will allow you to ride a motorcycle without a helmet if and only if you agree to become an organ donor, so we can harvest your organs after you crash and become brain-dead.' might shock them into wearing their helmet voluntarily, which seems like a simple good thing with no side effects aside from the possible organ donation discrediting.

Assuming that people are rational agents, some of whom experience large amounts of utility from riding without a helmet, we can expect the following:

1) People take the deal and ride without a helmet (good effect, social utility increases).
2) Motorcycle crashes decrease due to risk compensation (good effect).
3) The crashes that happen are more likely to cause death or brain injuries to motorcycle riders (bad effect).
4) More organs are available for use, both from the crash victims and people who take the deal and die from other causes (good effect).

I expect effects 3 and 4 to be the largest. More riders will die, or become disabled and require a lifetime of expensive medical care, and lives of people waiting for organ transplants will be saved.

Fatal motorcycle crashes tend to be an excellent source of donor organs, but I am fairly sure that most of them will cause a net decrease in QALYs, even the ones that result in many useful organs. Organ recipients tend to be old and unhealthy, so the organ does not give them that many QALYs. If the rider was young and healthy, they likely lose more QALYs than all the combined recipients of the organs would gain. If the rider was middle-aged and unhealthy, the organs are not likely to be as useful.

However, organ donation makes the QALY loss from those fatalities significantly less than most fatalities. (A nonfatal crash resulting in permanent disability will cause a larger QALY loss than a fatal crash.) If the rate of motorcycle crashes among people who take the deal is small enough, then the good effects of all of their organ donation will outweigh the bad effects of their crashes, and the policy is good.

I suspect that the crash rate is low enough that the policy of offering these exemptions is good. I think the breakeven would be something like ten new organ donors for every lethal or disabling crash, and I am almost sure that the lifetime risk of such a crash for motorcycle riders is significantly less than 10%.

Well-Being Analysis

This is a technical post, only of interest to people doing policy analysis. Much of it was originally written in response to a Facebook post related to Effective Altruism Policy Analytics, and I am reposting it here so it is in a more permanent place that I can link to.

One of the group members posted a link to the following paper:

Well-Being Analysis vs. Cost-Benefit Analysis

and asked me if we should consider using this method.

Briefly, well-being analysis is an attempt to measure the costs and benefits of a policy using a direct measure of human happiness or well-being, instead of using dollar values. My response was divided into two sections:

Well-being analysis in any form is not currently useful for people submitting policy comments:

1) Any comment that relied on a well-being analysis would likely be summarily dismissed. As the paper states, regulatory agencies are required by law to do cost-benefit analysis, and that is what they understand. They do not understand this new alien thing, have no way of judging its quality, and have no incentive to care about it.

2) The language of policy right now is monetary costs and benefits. We will influence policy by speaking the language currently used, and speaking it well. Lojban might be a better language than English, but it would be folly to submit comments in it.

3) There is a good chance that in 30 years, an overall framework like this will replace cost-benefit analysis and be required by law. This does not make it useful to us. 30 years ago, cost-benefit analysis was a bizarre fringe thing that nobody cared about and that would not affect policy in any way.

Well-bring analysis as presented in the paper has serious flaws:

4) Their example analysis dramatically undervalues monetary costs. They claim that "If that same individual's income decreased from $100,000 to $36,700, she would lose 0.11 WBUs" on a scale of 1 to 10. By comparison, "Unemployed individuals suffer a loss of 0.83 WBUs per year during the time that they remain unemployed." However, we know that people who have lost their jobs routinely choose to remain unemployed rather than settle for jobs that pay less than what they earned before.

4a) The data they use to determine well-being will systematically undervalue money, because they do a regression that separately counts the effects of money and health. Treating money and health as separate and exogenous is a grave error. Money and health are heavily correlated because money buys health. Most of what rich people do with their money is to rearrange their lives so they will be healthier and safer. When you take away money, health goes down. Their data on the 'well-being effect of income' is just the residual effect of money after 'correcting' for all the things that the money bought.

5) To a first approximation, the well-being analysis they present is just a way to ignore the compliance costs of any regulation that does not cause observable layoffs. Widespread use of this system in its current form would encourage a horde of very expensive regulations. Given how cheap it is for individuals to purchase lives with money, this is a huge problem. If 0.001% of the compliance costs of the EPA regulation they support would have otherwise gone to effective charities, the regulation has reduced well-being.

6) According to this analysis methodology in its current form, job losses dominate any consideration of economic efficiency, technological growth, or cheaper, better goods. Consistent use of it to analyze policies would result in support of policies that enforced stagnation on an economy, preventing almost any kind of innovation that had the potential to cause layoffs unless that innovation had an immediate and obvious health or safety benefit. Specifically, a "Ban computers" policy analyzed 40 years ago would probably score highly on a well-being analysis.

I do hope that, at some point in the future, all government actions will be evaluated on how they are expected to affect the well-being of all people, measured directly. Money is an imperfect measurement of how things affect well-being, because different people value money differently and a lot of important goods are hard to put money values on.

But right now, measuring things by their monetary impact, and the health and life impacts converted to their monetary values, is the best we can do. There is simply far more data available on money than there is on direct well-being measurements, and an imperfect system you can actually use is better than a theoretically ideal system that cannot be implemented.

Also, money is a much better measurement tool than most people realize. The money cost of doing a thing is a signal that includes a very good estimate of most of the resources consumed by that thing. Pollution and other externalities often make the money price of a thing an underestimate of its true cost, but in a competitive market it is rare for a money price to overestimate the cost of a thing. Whenever you think something should be cheap, but it costs a lot, that almost certainly means that you are missing something important. Regulators tend to miss important side effects and assume that things will be cheaper than they really are, because they only look sat narrow technical effects without considering larger impacts. Measuring the monetary impacts of a rule can correct for this.

However, as more research on well-being accumulates, and we get more data on how things affect quality of life, and we develop a better understanding of the complicated chains of causality that dominate all human interactions including the economy, we will probably move toward some kind of well-being analysis. The monetary costs and benefits will become a subset of the analysis. But it will likely take decades to get to that point.